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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydataset = {\n",
    "  'sites': [\"Google\", \"Runoob\", \"Wiki\"],\n",
    "  'number': [1, 2, 3]\n",
    "}\n",
    "\n",
    "myvar = pd.DataFrame(mydataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.DataFrame({\n",
    "    'length': [1.5, 0.5, 1.2, 0.9, 3],\n",
    "    'width': [0.7, 0.2, 0.15, 0.2, 1.1]\n",
    "    }, index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
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"
     },
     "metadata": {}
    }
   ],
   "source": [
    "hist = df4.hist(bins=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "     id    name             url  likes\n0  A001    菜鸟教程  www.runoob.com     61\n1  A002  Google  www.google.com    124\n2  A003      淘宝  www.taobao.com     45\n"
     ]
    }
   ],
   "source": [
    "URL = 'https://static.runoob.com/download/sites.json'\n",
    "df = pd.read_json(URL)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "id                 A001\n",
       "name               菜鸟教程\n",
       "url      www.runoob.com\n",
       "likes                61\n",
       "Name: 0, dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 103
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}